CN114987287B - Remaining driving range prediction method and device, vehicle and computer storage medium - Google Patents
Remaining driving range prediction method and device, vehicle and computer storage medium Download PDFInfo
- Publication number
- CN114987287B CN114987287B CN202210790953.7A CN202210790953A CN114987287B CN 114987287 B CN114987287 B CN 114987287B CN 202210790953 A CN202210790953 A CN 202210790953A CN 114987287 B CN114987287 B CN 114987287B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- real
- determining
- sub
- energy consumption
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000005265 energy consumption Methods 0.000 claims abstract description 88
- 238000012937 correction Methods 0.000 claims abstract description 61
- 230000005611 electricity Effects 0.000 claims abstract description 8
- 238000004891 communication Methods 0.000 claims description 15
- 230000011218 segmentation Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 description 8
- QYAPHLRPFNSDNH-MRFRVZCGSA-N (4s,4as,5as,6s,12ar)-7-chloro-4-(dimethylamino)-1,6,10,11,12a-pentahydroxy-6-methyl-3,12-dioxo-4,4a,5,5a-tetrahydrotetracene-2-carboxamide;hydrochloride Chemical compound Cl.C1=CC(Cl)=C2[C@](O)(C)[C@H]3C[C@H]4[C@H](N(C)C)C(=O)C(C(N)=O)=C(O)[C@@]4(O)C(=O)C3=C(O)C2=C1O QYAPHLRPFNSDNH-MRFRVZCGSA-N 0.000 description 4
- 102100026127 Clathrin heavy chain 1 Human genes 0.000 description 4
- 101000912851 Homo sapiens Clathrin heavy chain 1 Proteins 0.000 description 4
- 230000006399 behavior Effects 0.000 description 4
- 238000007405 data analysis Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 208000019901 Anxiety disease Diseases 0.000 description 2
- 230000036506 anxiety Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000035515 penetration Effects 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
- B60L2260/54—Energy consumption estimation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Navigation (AREA)
Abstract
The application provides a method and a device for predicting remaining driving range, a vehicle and a computer storage medium, wherein the method comprises the following steps: acquiring the real-time unit mileage energy consumption of a vehicle, the real-time residual capacity of an electricity storage device and the real-time position of the vehicle; determining an initial remaining driving range according to the real-time remaining electric quantity and the real-time unit mileage energy consumption; determining the congestion type of the area where the vehicle is located based on the real-time position, and determining a correction coefficient based on the real-time position and the congestion type; the initial remaining driving range is corrected by using the correction coefficient to obtain the target remaining driving range, and the prediction accuracy of the remaining driving range can be improved by adopting the method for predicting the remaining driving range.
Description
Technical Field
The present application relates to the technical field of vehicle engineering, and relates to, but is not limited to, a method and apparatus for predicting remaining driving range, a vehicle, and a computer storage medium.
Background
The new energy automobile is an automobile which adopts unconventional automobile fuel as a power source (or adopts conventional automobile fuel and a novel automobile-mounted power device) and integrates the advanced technology in the aspects of power control and driving of the automobile, and the formed technical principle is advanced, and the automobile has a new technology and a new structure.
A pure electric vehicle (BEV, battery Electric Vehicles) is a new energy vehicle, which uses a storage battery as an energy storage power source, and provides electric energy to an electric motor through the battery to drive the electric motor to run, thereby promoting the vehicle to run. The pure electric automobile has been widely accepted by users by virtue of the advantages of zero emission, high energy utilization rate, simple structure, small noise, wide raw materials and the like, and has entered a stage of rapid popularization. However, when the user uses the vehicle in daily life, the residual driving mileage is displayed in an inaccurate and full condition, and the displayed residual driving mileage is higher than the actual driving mileage, so that the vehicle brings great trouble to the user during actual use and causes the driving anxiety. The problem of inaccurate remaining driving range displayed by the pure electric vehicle has become one of the problems to be solved.
Disclosure of Invention
In view of the foregoing, embodiments of the present application provide a method, an apparatus, a vehicle, and a computer storage medium for predicting remaining driving range.
The technical scheme of the embodiment of the application is realized as follows:
the embodiment of the application provides a method for predicting remaining driving range, which comprises the following steps:
acquiring real-time unit mileage energy consumption of a vehicle, real-time residual capacity of a power storage device included in the vehicle and real-time position of the vehicle;
Determining an initial remaining driving range according to the real-time remaining electric quantity and the real-time unit mileage energy consumption;
determining the congestion type of the area where the vehicle is located based on the real-time position, and determining a correction coefficient based on the real-time position and the congestion type;
and correcting the initial remaining driving range by using the correction coefficient to obtain a target remaining driving range.
In some embodiments, the acquiring the real-time unit mileage energy consumption of the vehicle includes:
acquiring running record information of a road section with a preset length, which is recently run by a vehicle;
dividing the road section with the preset length into a plurality of sub-road sections;
obtaining average energy consumption of unit mileage of each sub-road section where the vehicle runs;
and determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section.
In some embodiments, the obtaining the average energy consumption of each sub-road section of the vehicle running in unit mileage includes:
acquiring the length of each sub-section obtained by segmentation;
acquiring the power consumption of each sub-section of the vehicle;
and determining the average energy consumption of the unit mileage of each sub-road section of the vehicle running according to the power consumption of each sub-road section and the length of the corresponding sub-road section.
In some embodiments, the determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road segment includes:
determining an influence factor corresponding to each sub-road section according to the driving time sequence of each sub-road section, wherein the influence factor represents the influence degree of each sub-road section on the current moment, and the influence factor corresponding to the sub-road section with the driving time sequence at the back is larger than the influence factor corresponding to the sub-road section with the driving time sequence at the front;
and determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section and the influence factors corresponding to each sub-road section.
In some embodiments, the determining the congestion type of the area where the vehicle is located based on the real-time location, and determining the correction coefficient based on the real-time location and the congestion type, includes:
determining a target center point closest to the vehicle from a plurality of preset center points according to the real-time position;
acquiring the congestion type of a target area where the target center point is located as the congestion type of the area where the vehicle is located;
and determining a correction coefficient according to the congestion type and the distance between the vehicle and the target center point.
In some embodiments, the obtaining the congestion type of the target area where the target center point is located as the congestion type of the area where the vehicle is located includes:
determining a target area where the target center point is located according to the acquired position information of the target center point;
inquiring a first relation table according to the target area to obtain the congestion type of the target area, wherein the first relation table stores the corresponding relation between a plurality of preset areas and the congestion type;
and determining the congestion type of the target area as the congestion type of the area where the vehicle is located.
In some embodiments, the congestion types include congestion, normal, and clear; and determining a correction coefficient according to the congestion type and the distance between the vehicle and the target center point, wherein the correction coefficient comprises the following components:
inquiring a second relation table according to the target area to obtain the area radius of the target area, wherein the second relation table stores the preset corresponding relation between a plurality of areas and the area radius;
determining a preset value corresponding to the congestion type according to the congestion type;
determining an initial coefficient according to the preset value, the regional radius and the distance between the vehicle and the target center point;
When the initial coefficient is smaller than a preset coefficient threshold value, determining the initial coefficient as a correction coefficient;
and when the initial coefficient is larger than or equal to a preset coefficient threshold value, determining the preset coefficient threshold value as a correction coefficient.
The embodiment of the application provides a remaining driving range prediction device, which comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the real-time unit mileage energy consumption of a vehicle, the real-time residual capacity of an electricity storage device included in the vehicle and the real-time position of the vehicle;
the first determining module is used for determining initial remaining driving range according to the real-time remaining electric quantity and the real-time unit mileage energy consumption;
the second determining module is used for determining the congestion type of the area where the vehicle is located based on the real-time position and determining a correction coefficient based on the real-time position and the congestion type;
and the correction module is used for correcting the initial remaining driving range by using the correction coefficient to obtain the target remaining driving range.
An embodiment of the present application provides a vehicle, including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
The memory is used for storing at least one executable instruction, and when the processor executes the executable instruction, the processor executes the steps of the residual driving range prediction method.
The embodiment of the application provides a computer storage medium, wherein at least one executable instruction is stored in the storage medium, and when the processor executes the executable instruction, the processor executes the steps of the residual driving range prediction method.
According to the method for predicting the remaining driving range, the real-time unit mileage energy consumption of the vehicle, the real-time remaining capacity of the power storage device included in the vehicle and the real-time position of the vehicle are obtained; determining an initial remaining driving range according to the real-time remaining electric quantity and the real-time unit mileage energy consumption; determining the congestion type of the area where the vehicle is located based on the real-time position, and determining a correction coefficient based on the real-time position and the congestion type; and correcting the initial remaining driving range by using the correction coefficient to obtain a target remaining driving range. The initial remaining driving range is determined through the real-time unit mileage energy consumption and the real-time remaining electric quantity of the vehicle, so that the initial remaining driving range is determined based on the real-time use condition of the vehicle, and the accuracy of the initial remaining driving range can be improved; according to the current real-time position of the vehicle, a dynamic correction coefficient related to the congestion type of the area is determined, the residual driving range is predicted based on the correction coefficient which changes in real time, and the prediction accuracy of the residual driving range can be improved. Therefore, the prediction accuracy of the remaining driving range can be improved by predicting the remaining driving range by considering the influence of the real-time driving condition of the vehicle on the energy consumption.
Drawings
In the drawings (which are not necessarily drawn to scale), like numerals may describe similar components in different views. The drawings illustrate generally, by way of example and not by way of limitation, various embodiments discussed herein.
Fig. 1 is a schematic flow chart of an implementation of a method for predicting remaining driving range according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of an implementation of the step of obtaining real-time unit mileage energy consumption of a vehicle in the method provided in the embodiment of the present application;
fig. 3 is a schematic flow chart of an implementation of a step of obtaining average energy consumption per unit mileage of each sub-section where a vehicle travels in the method provided in the embodiment of the present application;
FIG. 4 is a schematic flow chart of an implementation of the step of determining the energy consumption of a real-time unit mileage in the method according to the embodiment of the present application;
FIG. 5 is a schematic flow chart of an implementation of the step of determining a correction coefficient in the method according to the embodiment of the present application;
fig. 6 is a schematic implementation flow chart of a method for calculating remaining driving range of a pure electric vehicle according to an embodiment of the present application;
fig. 7 is a schematic diagram of a composition structure of a remaining driving range prediction apparatus according to an embodiment of the present application;
fig. 8 is a schematic view of a composition structure of a vehicle according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a particular ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a particular order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
Before the embodiments of the present application are described in detail, a method for predicting remaining driving range of a pure electric vehicle in related technologies is described.
At present, the most commonly used prediction method of the remaining driving range in the pure electric vehicle industry is the European driving test standard (NEDC, new European Driving Cycle), the prediction method is tested in a simple experimental environment, whether a vehicle-mounted accessory (such as an air conditioner and music) is started or not in the driving process of a user, whether traffic jam and the like affect the remaining driving range, and the prediction method of the NEDC simulation environment is not applicable to complex actual road conditions.
In practical application, when road traffic jam is serious, the idling ratio is high, the average speed is low, the vehicle mainly runs in a middle-low speed zone, the acceleration and the deceleration are frequent, and aiming at the defect of NEDC, the method for testing the remaining driving range based on the driving condition (CLTC, china light-duty vehicle test cycle) of a Chinese light vehicle is provided. Compared with NEDC, the CLTC increases road condition information with wider range, and the collected information comprises vehicle position information, power driving system information, battery information, emission and environment information. The method is characterized in that the method is divided into small segments according to idling and movement, and then the low speed, the medium speed and the high speed are further distinguished according to the maximum speed. Meanwhile, the low-frequency dynamic traffic volume big data of the corresponding city is required to be acquired, and a speed and flow model is established to obtain weights of different cities in each speed interval. Compared with NEDC, the CLTC improves the prediction accuracy of the residual driving range to a certain extent, but has larger acquisition granularity, and does not consider the actual driving condition of the user, so that the predicted residual driving range and the actual driving range still have larger gap.
In order to solve the above technical problems, an embodiment of the present application provides a method for predicting remaining driving range. The method provided in the embodiments of the present application is described below with reference to an apparatus for implementing the embodiments of the present application. Fig. 1 is a schematic flow chart of an implementation of a method for predicting remaining driving range according to an embodiment of the present application, where the method for predicting remaining driving range includes the following steps:
step S101, acquiring real-time unit mileage energy consumption of a vehicle, real-time residual capacity of an electricity storage device included in the vehicle and real-time position of the vehicle.
The method provided by the embodiment of the application can be realized by the residual driving range prediction device.
Real-time unit mileage energy consumption, i.e. the energy consumed by the vehicle per unit mileage at the current moment. In a pure electric vehicle application scenario, the unit mileage energy consumption may be the electric energy consumed per kilometer. In the embodiment of the application, the remaining driving range prediction device can acquire the real-time unit mileage energy consumption by combining the actual driving condition of the vehicle in a past period of time.
The real-time residual electric quantity is the electric quantity of the electric storage device (vehicle-mounted battery) of the vehicle at the current moment, and is all electric energy usable by the vehicle on the premise of no charging.
The real-time position of the vehicle can be combined with a global positioning system (GPS, global Positioning System) to obtain the position of the vehicle at the current moment.
Step S102, determining initial remaining driving range according to the real-time remaining power and the real-time unit mileage energy consumption.
In this embodiment of the present application, a quotient of the real-time remaining power and the real-time unit mileage energy consumption may be used as an initial remaining driving mileage, where a calculation formula is as follows:
wherein,,
and for the initial remaining driving range, Q is the real-time remaining electric quantity, and E is the real-time unit mileage energy consumption.
In the embodiment of the application, the initial remaining driving range is determined through the real-time unit mileage energy consumption and the real-time remaining electric quantity of the vehicle, the initial remaining driving range is determined based on the real-time service condition of the vehicle, and the initial remaining driving range has timeliness.
Step S103, determining the congestion type of the area where the vehicle is located based on the real-time position, and determining the correction coefficient based on the real-time position and the congestion type.
The congestion degree of different cities is different, and the congestion degree of vehicles at different positions in the same city is also different. In the embodiment of the application, a dynamic correction coefficient related to the congestion type of the area where the vehicle is located is determined according to the current real-time position of the vehicle so as to correct the initial remaining driving range. Compared with the fixed correction coefficient determined by only considering the congestion degree of the city in the prior art, the correction coefficient determined in the embodiment of the application has timeliness, the residual driving range is predicted based on the correction coefficient which changes in real time, and the prediction accuracy of the residual driving range can be improved.
And step S104, correcting the initial remaining driving range by using the correction coefficient to obtain the target remaining driving range.
In this embodiment of the present application, the product of the correction coefficient and the initial remaining driving range may be used as the predicted remaining driving range, i.e. the target remaining driving range, where the calculation formula is as follows:
s is the target remaining driving range, and T is the correction coefficient.
The method provided by the embodiment of the application comprises the following steps: acquiring the real-time unit mileage energy consumption of a vehicle, the real-time residual capacity of an electricity storage device and the real-time position of the vehicle; determining an initial remaining driving range according to the real-time remaining electric quantity and the real-time unit mileage energy consumption; determining the congestion type of the area where the vehicle is located based on the real-time position, and determining a correction coefficient based on the real-time position and the congestion type; and correcting the initial remaining driving range by using the correction coefficient to obtain the target remaining driving range. The initial remaining driving range is determined through the real-time unit mileage energy consumption and the real-time remaining electric quantity of the vehicle, so that the initial remaining driving range is determined based on the real-time use condition of the vehicle, and the accuracy of the initial remaining driving range can be improved; according to the current real-time position of the vehicle, a dynamic correction coefficient related to the congestion type of the area where the vehicle is located is determined, the residual driving range is predicted based on the correction coefficient which changes in real time, and the prediction accuracy of the residual driving range can be improved. Therefore, the prediction accuracy of the remaining driving range can be improved by predicting the remaining driving range by considering the influence of the real-time driving condition of the vehicle on the energy consumption.
In one implementation manner, the "obtaining the real-time unit mileage energy consumption of the vehicle" in step S101 in the embodiment shown in fig. 1 may be implemented by the steps shown in fig. 2:
step S201, acquiring travel record information of a road section of a preset length on which the vehicle has recently traveled.
The automobile running recorder, commonly called an automobile black box, is a digital electronic recording device which records and stores the running speed, time, mileage, power consumption and other state information related to the running of the automobile and can realize data output through an interface. In the embodiment of the application, the remaining driving range prediction device may acquire driving record information of the vehicle from a driving recorder of the vehicle. In practical application, the closer the behavior habit and the additional use condition of the user driving are to the current moment, the larger the influence on the real-time energy consumption is, and the farther the behavior habit and the additional use condition are to the current moment, the smaller the influence on the real-time energy consumption is. Based on this, when acquiring the running record information, the embodiment of the application may only acquire the running record information of the road section with the preset length on which the vehicle has recently run, where the running record information of the road section with the preset length has negligible influence on real-time energy consumption.
In step S202, the preset length road segment is divided into a plurality of sub-road segments.
In the acquired running record information of the road section with the preset length of the latest running, the closer to the current moment, the larger the influence on the real-time energy consumption is, and the farther to the current moment, the smaller the influence on the real-time energy consumption is. Based on the method, the road sections with the preset length which are recently driven are segmented, and the average energy consumption of unit mileage of each sub-road section is calculated according to the segmentation result.
Step S203, obtaining average energy consumption per unit mileage of each sub-road section where the vehicle runs.
And acquiring the total electric quantity consumed when the vehicle runs each sub-road section and the length of each sub-road section, and then determining the average energy consumption of the unit mileage of each sub-road section according to the electric consumption of each sub-road section and the length of the corresponding sub-road section. This step may be achieved by the following steps shown in fig. 3:
in step S2031, the length of each sub-link obtained by division is acquired.
For example, the preset length is denoted as L, divided into n segments, and the length of each sub-segment is denoted as L in the time sequence from the current time to the past 1 ，L 2 ，…，L n 。
In practical application, L can be determined according to test data analysis 1 ，L 2 ，…，L n Wherein L is the length of 1 ，L 2 ，…，L n Satisfy L 1 +L 2 +…+L n =l; or can be divided equally, in this case L 1 =L 2 =…=L n =
。
In step S2032, the power consumption of each sub-section on which the vehicle travels is acquired.
The driving record information includes power consumption, for each sub-link L i With the power consumption remaining at the start and end of the sub-linkThe difference between the electric quantities is determined to be e i . By using the method, the running L is obtained 1 ，L 2 ，…，L n The power consumption of the sub-sections are respectively denoted as e 1 ，e 2 ，…，e n 。
In step S2033, the average energy consumption per unit mileage of each sub-section for the vehicle to travel is determined according to the power consumption of each sub-section and the length of the corresponding sub-section.
In this embodiment of the present application, the quotient of the power consumption of the ith sub-road section and the length of the sub-road section may be used as the average energy consumption of the unit mileage of the vehicle running on the sub-road section, where the calculation formula is as follows:
wherein i takes on the values of 1,2, … and n.
And S204, determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section.
In the embodiment of the present application, the determination of the real-time unit mileage energy consumption may be implemented by the following steps shown in fig. 4:
in step S2041, according to the travel time sequence of each sub-link, an impact factor corresponding to each sub-link is determined.
The influence factors represent the influence degree of each sub-road section on the current moment, and the influence factors corresponding to the sub-road sections with the rear driving time sequence are larger than the influence factors of the sub-road sections with the front driving time sequence. Such as L 1 The sub-section is closest to the current time, and the degree of influence on the current time (denoted as W 1 ) Maximum, L n Furthest from the current time, the extent of influence on the current time (denoted as W n ) Minimum.
In the embodiment of the application, W 1 ，W 2 ，…，W n Can be determined experimentally, wherein W 1 ，W 2 ，…，W n Satisfy W 1 +W 2 +…+W n =1。
Step S2042, determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section and the influence factors corresponding to each sub-road section.
In this embodiment of the present application, a weighted sum of the average energy consumption of unit mileage of each sub-road section and the impact factor corresponding to each sub-road section may be used as the real-time energy consumption of unit mileage, where the calculation formula is as follows:
in the embodiment of the application, a section of the past preset driving distance is split into a plurality of sections, the average energy consumption of the unit mileage of each section of distance is calculated, and by combining big data analysis, a proper weight coefficient is set for the average energy consumption of the unit mileage of each section of distance, and the real-time unit mileage energy consumption of the vehicle is calculated.
In one implementation manner, "determining the congestion type of the area where the vehicle is located based on the real-time location and determining the correction coefficient based on the real-time location and the congestion type" in step S103 in the embodiment shown in fig. 1 may be implemented by the steps shown in fig. 5:
In step S501, a target center point closest to the vehicle is determined from a plurality of preset center points according to the real-time position.
In the embodiment of the application, the current real-time position of the vehicle can be acquired through a GPS or Beidou positioning system. The database is pre-stored with a relation table for storing the information of the center points, and the relation table is stored with a plurality of pre-calibrated center points and the position information of each center point. According to the Euclidean distance calculation formula, the distance between the real-time position of the vehicle and each center point is calculated, and the center point closest to the vehicle is selected as the target center point.
Step S502, obtaining the congestion type of the target area where the target center point is located as the congestion type of the area where the vehicle is located.
The area may be specifically a city, where the target area is the city where the target center point is located, and the target area where the target center point is located may be determined according to the location information of the target center point. In general, the city in which the target center point is located is the same city as the city in which the vehicle is currently located. However, when the vehicle is driving on the edge of city a, the distance from the center point of city B may be smaller than the distance from the center point of city a, and at this time, the city where the target center point is located is not the same city as the city where the vehicle is currently sitting, and the city where the target center point is visible is not necessarily the city where the vehicle is currently located.
After a target area of a target center point is determined, a first relation table is queried according to the target area to obtain the congestion type of the target area, the congestion type of the target area is determined to be the congestion type of the area where the vehicle is located, and the first relation table stores the corresponding relation between a plurality of preset areas and the congestion type. The data in the first relationship table may be updated manually or automatically in combination with the actual situation. In practical application, the central point and the congestion type of each city can be preset by combining the traffic flow and the congestion index issued by a third party platform, wherein the congestion type can comprise congestion, normal and unblocked. After calibration is completed, the center point information and the congestion type may be stored in a database. In practical application, the relationship table for storing the central point information and the first relationship table for storing the congestion type may be different data tables or the same data table.
Step S503, determining a correction coefficient according to the congestion type and the distance between the vehicle and the target center point.
And inquiring a second relation table according to the target area to obtain the area radius of the target area. And determining a preset value corresponding to the congestion type according to the congestion type. The second relation table stores the corresponding relation between each region and the region radius. Inquiring a second relation table according to the city where the target center point is located to obtain the city radius of the city, and marking as R, wherein the larger the general city is, the larger the city radius R is; the closer to the target center point, the greater the degree of congestion. When the congestion type is congestion, determining a corresponding preset value as a1, when the congestion type is normal, determining a corresponding preset value as a2, and when the congestion type is smooth, determining a corresponding preset value as a3, wherein a1< a2< a3.
And determining an initial coefficient according to the preset value, the regional radius and the distance between the vehicle and the target center point. The initial coefficient t1=a1+0.1×d/R for congested cities, the initial coefficient t2=a2+0.1×d/R for normal cities, and the initial coefficient t3=a3+0.1×d/R for unblocked cities. Where d is the distance between the vehicle and the target center point.
After determining the initial coefficient, judging whether the initial coefficient is smaller than a preset coefficient threshold value, and determining the initial coefficient as a correction coefficient when the initial coefficient is smaller than the preset coefficient threshold value; and when the initial coefficient is larger than or equal to the preset coefficient threshold value, determining the preset coefficient threshold value as a correction coefficient. Judgment T j Whether or not it is smaller than a preset coefficient threshold, if so, t=t j If greater than or equal to, t=preset coefficient threshold, j=1, 2,3.
In practical applications, a1, a2 and a3 may have values smaller than 1 and close to 1, for example a1=0.85, a2=0.9 and a3=0.95, and the preset coefficient threshold may have a value slightly larger than 1, for example 1.05.
In the embodiment of the application, the significant influence of traffic jam on energy consumption is considered, so that the position correction coefficient is introduced, the correction coefficient is determined according to the real-time position of the vehicle and the urban congestion condition, and compared with the fixed correction coefficient determined by only considering the urban congestion degree in the prior art, the determined correction coefficient has timeliness, the residual driving range is predicted based on the correction coefficient which changes in real time, and the prediction accuracy of the residual driving range can be improved.
In the following, an exemplary application of the embodiments of the present application in a practical application scenario will be described.
Under the common efforts of national macro-policy guidelines and industries, chinese electric vehicles rapidly develop in recent years, the market penetration rate of electric vehicles reaches 15% in 2021 years, and the partial single-month penetration rate exceeds 20%, which indicates that the electric vehicles are widely accepted by users and enter a stage of rapid popularization. However, when the user uses the electric vehicle daily, the residual cruising mileage of the electric vehicle is not accurately displayed and is not fully saturated, some algorithms estimate cruising based on the CLTC label, the calculated result is obviously higher than the actual cruising mileage of the vehicle, great trouble is brought to the user during actual use, and the mileage anxiety is increased.
In order to solve the technical problems, the embodiment of the application constructs a set of algorithm, predicts the future electricity consumption based on the behavior habit of driving a user, the use condition of a vehicle-mounted accessory, the current geographical position and other key information affecting the electricity consumption, obtains the current battery information through a battery management system, further calculates the initial driving range, and finally corrects the calculated driving range after obtaining the current vehicle position information through a GPS to obtain the accurate remaining driving range.
Fig. 6 is a schematic implementation flow chart of a method for calculating remaining driving range of a pure electric vehicle according to an embodiment of the present application, as shown in fig. 6, and the method includes the following steps:
step S601, rolling and splitting a past fixed driving distance into a plurality of sections, and calculating the unit energy consumption of each section of distance.
Dividing the distance L travelled in the past into n segments, respectively denoted as L in time sequence from the present to the past 1 ，L 2 ，…，L n . The vehicle controller (corresponding to the residual driving distance prediction device) calculates the average energy consumption of each of the n sections, and the average energy consumption is recorded as E 1 ，E 2 ，…，E n The unit is kwh/km. Over time, the overall vehicle controller needs to control E 1 ，E 2 ，…，E n Rolling update is performed.
Step S602, according to big data analysis, setting a proper weight coefficient for unit energy consumption of each distance, and comprehensively calculating the weighted unit energy consumption by the whole vehicle controller according to the information.
Through experiments and actual data analysis and research of users, the weight coefficient corresponding to each distance is determined, the weight coefficient is set to be larger when the weight coefficient is closer to the current moment, otherwise, the weight coefficient is smaller, and the set of coefficients are preset in the whole vehicle controller after the completion of the determination and are not changed. The weight coefficient corresponding to the average energy consumption of each distance is recorded as W 1 ，W 2 ，…，W n The weighted energy consumption E is calculated through the weight coefficient(i.e., the real-time unit mileage energy consumption above), the calculation formula is as follows:
E=E 1 *W 1 +E 2 *W 2 +E 3 *W 3 ……E n *W n （5）；
step S603, the whole vehicle controller obtains the current battery state.
The vehicle controller obtains the current battery state from the BMS, and calculates the residual available electric quantity Q (namely the real-time residual electric quantity) in the battery, wherein the unit is kwh.
In step S604, the vehicle controller calculates the initial remaining driving range according to the above information.
According to the information in S602 and S603, the initial remaining driving range is calculated
The calculation formula is as follows:
step S605, the whole vehicle controller obtains the current vehicle position information and obtains a position correction coefficient according to the position information.
And calculating a position correction coefficient T. And acquiring the current vehicle position through a GPS or Beidou positioning system, and calculating the distance d from the nearest city center point according to the position information, wherein the center point is calibrated in the whole vehicle controller in advance. The city is classified into three types of special congestion city, congestion city and non-congestion city according to the congestion degree, the city types are calibrated in the whole vehicle controller after being determined, the actual situation is changed, and the updating is carried out through OTA upgrading. And marking the city radius as R in the whole vehicle controller. When the nearest city center belongs to a particularly congested city, the position correction factor t=0.85+0.1×d/R (T < 1.05). When the nearest city center belongs to the congested city, the position correction coefficient is t=0.9+0.1×d/R (T < 1.05). When the nearest city center belongs to an uncongested city, the position correction factor t=0.95+0.1×d/R (T < 1.05).
Step S606, calculating to obtain the final remaining driving range according to the initial driving range and the position correction coefficient.
According to the initial driving range and the position correction coefficient obtained in S604 and S605, the final remaining driving range S (i.e. the target remaining driving range above) is calculated as follows:
the method provided by the embodiment of the application can be used for preliminarily estimating the remaining driving mileage based on the driving behavior habit and the vehicle-mounted accessory service condition of the past period, and the weight coefficients of different periods are set differently, so that different weight coefficients are set in the sequence from the current moment to the distant moment, and compared with other calculation methods, the method has better accuracy. Meanwhile, the obvious influence of traffic jam on energy consumption is considered, so that a position correction coefficient is introduced, the remaining driving range is corrected again according to the driving position of the vehicle and urban congestion, and the prediction accuracy is improved.
Based on the foregoing embodiments, the embodiments of the present application provide a remaining driving range prediction apparatus, where each module included in the apparatus and each unit included in each module may be implemented by a processor in a computer device; of course, the method can also be realized by a specific logic circuit; in practice, the processor may be a central processing unit (CPU, central Processing Unit), a microprocessor (MPU, microprocessor Unit), a digital signal processor (DSP, digital Signal Processing), or a field programmable gate array (FPGA, field Programmable Gate Array), or the like.
Fig. 7 is a schematic structural diagram of the remaining driving range prediction device provided in the embodiment of the present application, as shown in fig. 7, where the remaining driving range prediction device 700 includes:
an obtaining module 701, configured to obtain real-time unit mileage energy consumption of a vehicle, real-time residual capacity of an electricity storage device included in the vehicle, and a real-time position of the vehicle;
a first determining module 702, configured to determine an initial remaining driving range according to the real-time remaining power and the real-time unit mileage energy consumption;
a second determining module 703, configured to determine a congestion type of an area where the vehicle is located based on the real-time location, and determine a correction coefficient based on the real-time location and the congestion type;
and the correction module 704 is configured to correct the initial remaining driving range by using the correction coefficient to obtain a target remaining driving range.
In some embodiments, the obtaining module 701 is further configured to:
acquiring running record information of a road section with a preset length, which is recently run by a vehicle;
dividing the road section with the preset length into a plurality of sub-road sections;
obtaining average energy consumption of unit mileage of each sub-road section where the vehicle runs;
And determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section.
In some embodiments, the obtaining module 701 is further configured to:
acquiring the length of each sub-section obtained by segmentation;
acquiring the power consumption of each sub-section of the vehicle;
and determining the average energy consumption of the unit mileage of each sub-road section of the vehicle running according to the power consumption of each sub-road section and the length of the corresponding sub-road section.
In some embodiments, the obtaining module 701 is further configured to:
determining an influence factor corresponding to each sub-road section according to the driving time sequence of each sub-road section, wherein the influence factor represents the influence degree of each sub-road section on the current moment, and the influence factor corresponding to the sub-road section with the driving time sequence at the back is larger than the influence factor corresponding to the sub-road section with the driving time sequence at the front;
and determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section and the influence factors corresponding to each sub-road section.
In some embodiments, the second determining module 703 is further configured to:
determining a target center point closest to the vehicle from a plurality of preset center points according to the real-time position;
acquiring the congestion type of a target area where the target center point is located as the congestion type of the area where the vehicle is located;
And determining a correction coefficient according to the congestion type and the distance between the vehicle and the target center point.
In some embodiments, the second determining module 703 is further configured to:
determining a target area where the target center point is located according to the acquired position information of the target center point;
inquiring a first relation table according to the target area to obtain the congestion type of the target area, wherein the first relation table stores the corresponding relation between a plurality of preset areas and the congestion type;
and determining the congestion type of the target area as the congestion type of the area where the vehicle is located.
In some embodiments, the congestion types include congestion, normal, and clear;
the second determining module 703 is further configured to:
inquiring a second relation table according to the target area to obtain the area radius of the target area, wherein the second relation table stores the preset corresponding relation between a plurality of areas and the area radius;
determining a preset value corresponding to the congestion type according to the congestion type;
determining an initial coefficient according to the preset value, the regional radius and the distance between the vehicle and the target center point;
When the initial coefficient is smaller than a preset coefficient threshold value, determining the initial coefficient as a correction coefficient;
and when the initial coefficient is larger than or equal to a preset coefficient threshold value, determining the preset coefficient threshold value as a correction coefficient.
It should be noted here that: the description of the remaining range prediction apparatus embodiment items above is similar to the method description above, with the same advantageous effects as the method embodiment. For technical details not disclosed in the embodiments of the remaining driving range prediction apparatus of the present application, those skilled in the art will understand with reference to the description of the embodiments of the method of the present application.
In the embodiment of the present application, if the method is implemented in the form of a software functional module, and sold or used as a separate product, the method may also be stored in a computer storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partly contributing to the prior art, and the computer software product may be stored in a storage medium, and include several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a computer storage medium having at least one executable instruction stored therein, where the executable instruction causes a processor to execute the steps in the remaining range prediction method provided in the above embodiment.
In the embodiment of the present application, a remaining driving range prediction device, such as a pure electric vehicle, fig. 8 is a schematic diagram of a composition structure of a vehicle provided in the embodiment of the present application, and other exemplary structures of the vehicle 800 may be foreseen according to the exemplary structure of the vehicle 800 shown in fig. 8, so that the structures described herein should not be considered as limitations, for example, some components described below may be omitted, or components not described below may be added to adapt to specific requirements of some applications.
The vehicle 800 shown in fig. 8 includes: a processor 801, at least one communication bus 802, a user interface 803, at least one external communication interface 804, and memory 805. Wherein the communication bus 802 is configured to enable connected communication between these components. The user interface 803 may include a display screen, and the external communication interface 804 may include a standard wired interface and a wireless interface, among others. Wherein the processor 801 is configured to execute a program of the remaining range prediction method stored in the memory, so as to implement the steps in the remaining range prediction method provided in the above embodiment.
The description of the vehicle and the storage medium embodiments above is similar to that of the method embodiments described above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the vehicle and storage medium embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
Alternatively, the integrated units described above may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partly contributing to the prior art, embodied in the form of a software product stored in a storage medium, including several instructions for causing an apparatus to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
The foregoing is merely an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method for predicting remaining range, the method comprising:
Acquiring real-time unit mileage energy consumption of a vehicle, real-time residual capacity of a power storage device included in the vehicle and real-time position of the vehicle;
determining an initial remaining driving range according to the real-time remaining electric quantity and the real-time unit mileage energy consumption;
determining a target center point closest to the vehicle from a plurality of preset center points according to the real-time position;
acquiring the congestion type of a target area where the target center point is located as the congestion type of the area where the vehicle is located; wherein the congestion types include congestion, normal and clear;
determining a correction coefficient according to the congestion type, the area radius of the target area and the distance between the vehicle and the target center point;
and correcting the initial remaining driving range by using the correction coefficient to obtain a target remaining driving range.
2. The method of claim 1, wherein the obtaining real-time unit mileage energy consumption of the vehicle comprises:
acquiring running record information of a road section with a preset length, which is recently run by a vehicle;
dividing the road section with the preset length into a plurality of sub-road sections;
obtaining average energy consumption of unit mileage of each sub-road section where the vehicle runs;
And determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section.
3. The method of claim 2, wherein the obtaining the average energy consumption per unit mileage for each sub-section of the vehicle traveling comprises:
acquiring the length of each sub-section obtained by segmentation;
acquiring the power consumption of each sub-section of the vehicle;
and determining the average energy consumption of the unit mileage of each sub-road section of the vehicle running according to the power consumption of each sub-road section and the length of the corresponding sub-road section.
4. The method of claim 2, wherein determining the real-time unit mileage energy consumption based on the unit mileage average energy consumption of each sub-section comprises:
determining an influence factor corresponding to each sub-road section according to the driving time sequence of each sub-road section, wherein the influence factor represents the influence degree of each sub-road section on the current moment, and the influence factor corresponding to the sub-road section with the driving time sequence at the back is larger than the influence factor corresponding to the sub-road section with the driving time sequence at the front;
and determining the real-time unit mileage energy consumption according to the unit mileage average energy consumption of each sub-road section and the influence factors corresponding to each sub-road section.
5. The method according to claim 1, wherein the obtaining the congestion type of the target area where the target center point is located as the congestion type of the area where the vehicle is located includes:
Determining a target area where the target center point is located according to the acquired position information of the target center point;
inquiring a first relation table according to the target area to obtain the congestion type of the target area, wherein the first relation table stores the corresponding relation between a plurality of preset areas and the congestion type;
and determining the congestion type of the target area as the congestion type of the area where the vehicle is located.
6. The method of claim 5, wherein the determining a correction factor based on the congestion type, the area radius of the target area, and the distance between the vehicle and the target center point comprises:
inquiring a second relation table according to the target area to obtain the area radius of the target area, wherein the second relation table stores the preset corresponding relation between a plurality of areas and the area radius;
determining a preset value corresponding to the congestion type according to the congestion type;
determining an initial coefficient according to the preset value, the regional radius and the distance between the vehicle and the target center point;
when the initial coefficient is smaller than a preset coefficient threshold value, determining the initial coefficient as a correction coefficient;
And when the initial coefficient is larger than or equal to a preset coefficient threshold value, determining the preset coefficient threshold value as a correction coefficient.
7. A remaining range prediction apparatus, the apparatus comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the real-time unit mileage energy consumption of a vehicle, the real-time residual capacity of an electricity storage device included in the vehicle and the real-time position of the vehicle;
the first determining module is used for determining initial remaining driving range according to the real-time remaining electric quantity and the real-time unit mileage energy consumption;
the second determining module is used for determining a target center point closest to the vehicle from a plurality of preset center points according to the real-time position; acquiring the congestion type of a target area where the target center point is located as the congestion type of the area where the vehicle is located; wherein the congestion types include congestion, normal and clear; determining a correction coefficient according to the congestion type, the area radius of the target area and the distance between the vehicle and the target center point;
and the correction module is used for correcting the initial remaining driving range by using the correction coefficient to obtain the target remaining driving range.
8. A vehicle, characterized by comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that, when executed by the processor, performs the steps of the remaining range prediction method of any one of claims 1 to 6.
9. A computer storage medium having stored therein at least one executable instruction which, when executed by a processor, performs the steps of the remaining range prediction method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210790953.7A CN114987287B (en) | 2022-07-05 | 2022-07-05 | Remaining driving range prediction method and device, vehicle and computer storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210790953.7A CN114987287B (en) | 2022-07-05 | 2022-07-05 | Remaining driving range prediction method and device, vehicle and computer storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114987287A CN114987287A (en) | 2022-09-02 |
| CN114987287B true CN114987287B (en) | 2023-06-27 |
Family
ID=83020232
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210790953.7A Active CN114987287B (en) | 2022-07-05 | 2022-07-05 | Remaining driving range prediction method and device, vehicle and computer storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114987287B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115675179A (en) * | 2022-09-28 | 2023-02-03 | 一汽奔腾轿车有限公司 | Method and system for displaying driving range of pure electric vehicle |
| CN115593234B (en) * | 2022-09-30 | 2024-04-19 | 重庆赛力斯凤凰智创科技有限公司 | Method, device, computer equipment and storage medium for calculating apparent driving range |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100828796B1 (en) * | 2007-02-15 | 2008-05-09 | 현대자동차주식회사 | Calculation method of distance to empty of the trip computer |
| WO2013108246A2 (en) * | 2012-01-17 | 2013-07-25 | Better Place GmbH | Approximation of remaining travelable distance of a vehicle powered by a battery |
| CN105235543A (en) * | 2015-10-27 | 2016-01-13 | 北京新能源汽车股份有限公司 | Method, device and system for processing remaining driving mileage of electric automobile |
| CN105711592A (en) * | 2016-04-27 | 2016-06-29 | 蔚来汽车有限公司 | Method for adjusting self-adaption driving behavior of electric automobile |
| DE102016214997A1 (en) * | 2016-08-11 | 2018-02-15 | Bayerische Motoren Werke Aktiengesellschaft | Method for operating a motor vehicle with a fuel cell system and with at least one energy storage device |
| CN107976635A (en) * | 2017-11-17 | 2018-05-01 | 厦门大学 | A kind of electric automobile residue course continuation mileage evaluation method based on Kalman filtering |
| CN108734876A (en) * | 2017-04-13 | 2018-11-02 | 宁波轩悦行电动汽车服务有限公司 | It is a kind of that method of hiring a car is preengage based on the backstage for estimating electricity and stand-by period |
| CN108790860A (en) * | 2018-04-18 | 2018-11-13 | 浙江吉利汽车研究院有限公司 | Mileage computational methods, system and automobile |
| CN109466333A (en) * | 2018-11-14 | 2019-03-15 | 哈尔滨理工大学 | Remaining mileage calculation method based on the estimation of electric motor coach dynamic process quality |
| CN111452619A (en) * | 2020-01-22 | 2020-07-28 | 北京理工大学 | Online energy consumption prediction method and system for electric vehicle |
| CN112078424A (en) * | 2019-06-13 | 2020-12-15 | 张丽 | New energy automobile battery driving mileage estimation device and estimation method thereof |
| CN112208338A (en) * | 2020-09-29 | 2021-01-12 | 广州小鹏自动驾驶科技有限公司 | Method and device for calculating remaining endurance mileage of destination |
| CN112304328A (en) * | 2020-10-09 | 2021-02-02 | 恒大新能源汽车投资控股集团有限公司 | Method and device for predicting driving mileage of electric vehicle and computer readable storage medium |
| CN112319307A (en) * | 2020-10-30 | 2021-02-05 | 东风襄阳旅行车有限公司 | K value-based electric automobile driving range estimation method |
| CN112406630A (en) * | 2020-11-30 | 2021-02-26 | 的卢技术有限公司 | Electric vehicle driving range calculation method based on working condition identification |
| KR20210079569A (en) * | 2019-12-20 | 2021-06-30 | 숭실대학교산학협력단 | Apparatus and method for predicting driving distance according to driving mode of electric vehicle |
| CN114347854A (en) * | 2021-05-24 | 2022-04-15 | 长城汽车股份有限公司 | Method, device, controller, medium and vehicle for determining driving range of electric vehicle |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150158397A1 (en) * | 2013-12-05 | 2015-06-11 | Ford Global Technologies, Llc | Method and Apparatus for Predicting Electric Vehicle Energy Consumption |
| FR3018921B1 (en) * | 2014-03-24 | 2017-07-07 | Renault Sas | METHOD FOR ESTIMATING THE AUTONOMY OF AN ELECTRIC OR HYBRID VEHICLE |
| US9539904B2 (en) * | 2014-07-01 | 2017-01-10 | Ford Global Technologies, Llc | Energy consumption rate in distance domain |
| US11043043B2 (en) * | 2018-11-17 | 2021-06-22 | International Business Machines Corporation | Dynamic driving range prediction for electric vehicles |
-
2022
- 2022-07-05 CN CN202210790953.7A patent/CN114987287B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100828796B1 (en) * | 2007-02-15 | 2008-05-09 | 현대자동차주식회사 | Calculation method of distance to empty of the trip computer |
| WO2013108246A2 (en) * | 2012-01-17 | 2013-07-25 | Better Place GmbH | Approximation of remaining travelable distance of a vehicle powered by a battery |
| CN105235543A (en) * | 2015-10-27 | 2016-01-13 | 北京新能源汽车股份有限公司 | Method, device and system for processing remaining driving mileage of electric automobile |
| CN105711592A (en) * | 2016-04-27 | 2016-06-29 | 蔚来汽车有限公司 | Method for adjusting self-adaption driving behavior of electric automobile |
| DE102016214997A1 (en) * | 2016-08-11 | 2018-02-15 | Bayerische Motoren Werke Aktiengesellschaft | Method for operating a motor vehicle with a fuel cell system and with at least one energy storage device |
| CN108734876A (en) * | 2017-04-13 | 2018-11-02 | 宁波轩悦行电动汽车服务有限公司 | It is a kind of that method of hiring a car is preengage based on the backstage for estimating electricity and stand-by period |
| CN107976635A (en) * | 2017-11-17 | 2018-05-01 | 厦门大学 | A kind of electric automobile residue course continuation mileage evaluation method based on Kalman filtering |
| CN108790860A (en) * | 2018-04-18 | 2018-11-13 | 浙江吉利汽车研究院有限公司 | Mileage computational methods, system and automobile |
| CN109466333A (en) * | 2018-11-14 | 2019-03-15 | 哈尔滨理工大学 | Remaining mileage calculation method based on the estimation of electric motor coach dynamic process quality |
| CN112078424A (en) * | 2019-06-13 | 2020-12-15 | 张丽 | New energy automobile battery driving mileage estimation device and estimation method thereof |
| KR20210079569A (en) * | 2019-12-20 | 2021-06-30 | 숭실대학교산학협력단 | Apparatus and method for predicting driving distance according to driving mode of electric vehicle |
| CN111452619A (en) * | 2020-01-22 | 2020-07-28 | 北京理工大学 | Online energy consumption prediction method and system for electric vehicle |
| CN112208338A (en) * | 2020-09-29 | 2021-01-12 | 广州小鹏自动驾驶科技有限公司 | Method and device for calculating remaining endurance mileage of destination |
| CN112304328A (en) * | 2020-10-09 | 2021-02-02 | 恒大新能源汽车投资控股集团有限公司 | Method and device for predicting driving mileage of electric vehicle and computer readable storage medium |
| CN112319307A (en) * | 2020-10-30 | 2021-02-05 | 东风襄阳旅行车有限公司 | K value-based electric automobile driving range estimation method |
| CN112406630A (en) * | 2020-11-30 | 2021-02-26 | 的卢技术有限公司 | Electric vehicle driving range calculation method based on working condition identification |
| CN114347854A (en) * | 2021-05-24 | 2022-04-15 | 长城汽车股份有限公司 | Method, device, controller, medium and vehicle for determining driving range of electric vehicle |
Non-Patent Citations (1)
| Title |
|---|
| 基于时空消耗理论的城市交通结构数学规划优化模型研究;赵发科等;《现代交通技术》;第9卷(第04期);第62-65、73页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114987287A (en) | 2022-09-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114987287B (en) | Remaining driving range prediction method and device, vehicle and computer storage medium | |
| CN111806239B (en) | Electric vehicle endurance mileage measuring method, electronic device, and storage medium | |
| CN111497679B (en) | Pure electric vehicle energy consumption monitoring optimization method and system | |
| CN110126841B (en) | Pure electric vehicle energy consumption model prediction method based on road information and driving style | |
| Wang et al. | Battery electric vehicle energy consumption prediction for a trip based on route information | |
| CN110103719B (en) | Charging prompting method and system of electric automobile and vehicle | |
| CN111452619B (en) | Online energy consumption prediction method and system for electric vehicle | |
| US20180370537A1 (en) | System providing remaining driving information of vehicle based on user behavior and method thereof | |
| US10829065B1 (en) | Vehicle power management system | |
| CN111422099B (en) | Endurance mileage estimation method for pure electric vehicle | |
| CN111670340A (en) | Method for acquiring remaining driving mileage of vehicle, electronic equipment and vehicle | |
| CN110329266A (en) | Can course continuation mileage determine method, apparatus, terminal and storage medium | |
| CN112406630B (en) | Electric vehicle driving mileage calculation method based on working condition recognition | |
| CN111452794A (en) | Method and device for determining energy consumption and method and device for determining driving strategy | |
| JP2023535828A (en) | Routing method, device, equipment and medium | |
| CN113536518A (en) | Method for estimating remaining driving range of pure electric vehicle | |
| CN108985966B (en) | Electric quantity consumption calculation method and device, vehicle and computer readable storage medium | |
| CN111137169B (en) | Estimation method and device of endurance mileage | |
| CN112208337B (en) | Mileage deviation correcting method and device for electric two-wheeled vehicle and electric two-wheeled vehicle | |
| CN112083332A (en) | Pure electric vehicle driving range estimation method considering user experience | |
| CN116853001A (en) | Vehicle endurance mileage determination method and device, electronic equipment and storage medium | |
| JP2012123518A (en) | Vehicle information management server, vehicle information management system and vehicle information management method | |
| CN115123251A (en) | Vehicle endurance mileage prediction method and device, electronic equipment and storage medium | |
| CN113469455A (en) | Prediction method and device of endurance data | |
| CN113362488B (en) | OBD monitoring data processing system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |